Humans have developed the capability to teach relevant aspects of new or adapted tasks to a social peer with very few task demonstrations by making use of scaffolding strategies that leverage prior knowledge and importantly prior joint experience to yield a joint understanding and a joint execution of the required steps to solve the task. This process has been discovered and analyzed in parent-infant interaction and constitutes a ``co-construction'' as it allows both, the teacher and the learner, to jointly contribute to the task. We propose to focus research in robot interactive learning on this co-construction process to enable robots to learn from non-expert users in everyday situations. In the following, we will review current proposals for interactive task learning and discuss their main contributions with respect to the entailing interaction. We then discuss our notion of co-construction and summarize research insights from adult-child and human-robot interactions to elucidate its nature in more detail. From this overview we finally derive research desiderata that entail the dimensions architecture, representation, interaction and explainability.

Sifting through job applications, analyzing X-ray images, suggesting a new track list--interaction between humans and machines has become an integral part of modern life. The basis for these artificial intelligence (AI) processes is algorithmic decision-making. However, as these are generally difficult to understand, they often prove less useful than anticipated. Researchers at Paderborn and Bielefeld University are hoping to change this, and are discussing how the explainability of artificial intelligence can be improved and adapted to the needs of human users. Their work has recently been published in the respected journal IEEE Transactions on Cognitive and Developmental Systems.